Table REF  and Table REF  compare BCNet with other SOTA amodal segmentation methods on both the COCOA [1]} and KINS [2]} datasets, where: 1) AmodalMask [1]} directly predicts amodal masks from image patches; 2) Occlusion RCNN (ORCNN) [4]} is an extension of Mask R-CNN with both amodal and modal mask heads; 3) ASN module [2]} contains additional occlusion classification branch and multi-level coding.
Compared to these occlusion handling approaches, our bilayer GCN with cascaded structure still performs favorably against the state-of-the-art methods, which shows the effectiveness of BCNet in decoupling overlapping objects and mask completion under the amodal segmentation setting.
Figure REF  and Figure REF  show the qualitative comparison on COCOA and KINS respectively.
